Assessing the Value of Real-Time Data for the Dynamic Scheduling of In-Plant Logistics Activities
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Publisher
Springer Nature Switzerland
Link
https://link.springer.com/content/pdf/10.1007/978-3-031-56826-8_21
Reference17 articles.
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3. Liu, R., Piplani, R., Toro, C.: Deep reinforcement learning for dynamic scheduling of a flexible job shop. Int. J. Prod. Res. 60(13), 4049–4069 (2022)
4. Yan, Q., Wang, H., Wu, F.: Digital twin-enabled dynamic scheduling with preventive maintenance using a double-layer Q-learning algorithm. Comput. Oper. Res. 144 (2022)
5. Schmid, N.A., Limère, V.: A classification of tactical assembly line feeding problems. Int. J. Prod. Res. 57(24), 7586–7609 (2019)
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